About

Pooja Ahuja is an accomplished Data Scientist with a passion for crafting brains for machines. She has 6.37 years of relevant experience in the field of Machine Learning, Artificial Intelligence, and Deep Learning. Pooja is currently working as a Data Scientist at Hewlett Packard, where she is researching a Deep Learning approach by analyzing the gradients in backprop to create new rules to classify network devices. She has also researched and developed Rule Ranker to create a hierarchy amongst Networking rules to classify devices and a TCP classifier. Prior to joining Hewlett Packard, Pooja worked as a Data Scientist at Chewy, where she researched and took to production Recommendation strategies, worked on Frequently Bought Together, and implemented a novel mathematical approach. She also used machine learning to automate pricing and evaluation and won the first-ever company-wide Hackathon. Pooja was the first female Data Scientist at Chewy. Pooja has also worked as a Senior Machine Learning Engineer at Jask, where she researched and developed an LSTM model to differentiate Domain Generated Algorithms, models for sparse time series to analyze anomalies, label propagation for dealing with unbalanced sparse datasets, and payload detection and classification of logs. She was the first female Engineer and the 5th Engineer at Jask. Pooja holds a Master’s Degree with a Thesis in Machine Learning from the University of Nebraska-Lincoln. She also holds a degree from St. Joseph High School and Fergusson College. Pooja is proficient in Data Science, Research Science, ML, AI, Deep Learning, and AWS. Pooja is a highly motivated and innovative Data Scientist with a track record of success in developing cutting-edge solutions. She is passionate about using her skills and knowledge to craft brains for machines that can make a difference in the world.